public CNNLayer(int nodeNum, ACTIVE_FUNCTION af = ACTIVE_FUNCTION.RELU)
{
mAFuction = af;
mNodeNum = nodeNum;
Random r = new Random();
mB = Matrix <double> .Build.Dense(nodeNum, 1, (i, j) => r.NextDouble());
mZ = Matrix <double> .Build.Dense(nodeNum, 1, (i, j) => r.NextDouble());
mA = Matrix <double> .Build.Dense(nodeNum, 1, (i, j) => r.NextDouble());
}
public static Matrix <double> getActiveValMatrix(Matrix <double> m, ACTIVE_FUNCTION af)
{
Matrix <double> re = Matrix <double> .Build.DenseIdentity(m.RowCount, m.ColumnCount);
try
{
re = Matrix <double> .Build.Dense(m.RowCount, m.ColumnCount, (i, j) => getActiveVal(m.Row(i)[j], af));
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
return(re);
}
public static double getActiveVal(double z, ACTIVE_FUNCTION af)
{
double re = 0.1;
try
{
if (af == ACTIVE_FUNCTION.RELU)
{
/*
* a = g(z) = max(0,z)
* dz = 0/1
*/
if (z > 0)
{
re = z;
}
else
{
re = 0;
}
}
if (af == ACTIVE_FUNCTION.LOGI)
{
/*
* a = g(z) = 1/(1+e(-z))
* dz = a(1-a)
*/
re = 1 / (1 + Math.Exp(-z));
}
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
return(re);
}
